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Applications of Some New Q-Rung Orthopair Fuzzy Distance Measures and Knowledge Measures in Classification and Decision-Making Problems
Yousef Al-Qudah, Abdul Haseeb Ganie, Ali Jaradat and Ajaz Ul Islam
Q-rung orthopair fuzzy sets excel in handling uncertain situations, surpassing the capabilities of fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, and Fermatean fuzzy sets. Fuzzy distance measures find diverse applications, including but not limited to cluster analysis, classification tasks, and even medical diagnosis. Within the context of multicriteria decision-making, fuzzy knowledge methods play a critical role in criteria weight determination. This paper introduces an innovative approach for developing novel distance measures for q-rung orthopair fuzzy sets. Additionally, we have thoroughly examined the appealing properties exhibited by these proposed distance measures. By the recommended distance methods, we have presented fresh knowledge methods for q-rung orthopair fuzzy sets. By linguistic hedging and numerical comparisons, we have empirically demonstrated the superiority of the presented knowledge and distance metrics over the current methods for q-rung orthopair fuzzy sets. The effectiveness of the proposed q-rung orthopair fuzzy distance metrics is further exemplified through pattern analysis. Lastly, we have devised a pioneering approach for multi-attribute decision-making within the framework of q-rung orthopair fuzzy sets. This addresses a significant limitation in the widely recognized decision-making method, specifically the method for order preference by similarity to the ideal solution.
Keywords: Distance measure, knowledge measure, multi-attribute decision-making, pattern recognition, Pythagorean fuzzy set, q-rung orthopair fuzzy set, t-conorm